A. L. Bogdanov

ECONOMETRIC ANALYSIS OF THE USED CAR MARKET

The object of this study is the used car market, the goal is to build a model for the formation of the price of a car for secondary market taking into account various factors. Two approaches to the construction of such a model have been proposed.

The object of this study is the used car market, the goal is to identify factors and assess the degree of their influence on the price of a used car. The data for the study was obtained from the auto.ru website, one of the largest Russian sites. automotive topics... The choice of this site is explained, firstly, by the fact that the site has a fairly large database of offers, and secondly, for each car sold in the database there is detailed information about its characteristics.

The size of the sample downloaded from the website (May 5, 2005) after deleting inaccurate and conflicting data amounted to 47,175 records for more than 700 models of 22 manufacturers. Most of the sample consists of proposals from Moscow (40434) and St. Petersburg (4690). The sample contains the following information about each car sold: manufacturer name (car brand), car model, year of manufacture, mileage, engine size, engine type (petrol / diesel), drive type (front / rear / full), body type, color , the possibility of bargaining, information about the vehicle configuration (the presence of a radio tape recorder, airbags, aBS systems and ESP, alarm, central locking, trim, etc., 58 points in total).

Dummy Variables Description

D2 Airbag lateral

D3 Airbag for driver

D4 Airbag for passenger

D5 Airbag window

DS Aut. ex. light

D9 Traction control

D10 Au diopreparation

D11 Roof rack

D12 Rear differential lock

D13 On-board computer

D15 D / about trunk

D16 D / o gas tank

D17 Rain sensor

D1S Immobilizer

D19 Catalyst

D20 Climate control

D21 Air conditioner

D22 Headlight range control

D23 Cruise control

D24 Xenon headlights

D25 Winch

D26 Light alloy wheels

D2S Navigation system

D29 Heated mirrors

DESCRIPTION OF VARIABLES

Let's introduce the notation: PRICE - car price (SUSA); AGE - age (number of years); PROBEG - mileage (lO OOO km); DRVOL - engine displacement; DIZEL - dummy variable denoting the type of engine (O - petrol, 1 - diesel); PT0, PT1, PTl - dummy variables indicating the type of drive (rear, front, four-wheel drive); NEW - equal to 1 for new cars and 0 for used ones; RU - equal to 1 if the car russian production, O - otherwise; KZ0, KZ1, ..., KZ12 - variables denoting the type of body (sedan, hatchback, station wagon, coupe, pickup, combi, convertible, minivan, stretch, roadster, targa, van, SUV); MO, M1, ..., M22 - dummy variables denoting the car brand (Audi, BMW, Daewoo, Dodge, Ford, Honda, Hyundai, Lexus, Mazda, Mercedes, Mitsubishi, Nissan, Opel, Peugeot, Renault, Subaru, Suzuki , Toyota, Volkswagen, Volvo, VAZ, GAZ); TORG - equal to 1 if the seller allows bargaining, and O - otherwise; Dl, D2, ..., D5S are dummy variables that take the value 1 if there is a corresponding option in the car and O otherwise. A complete description of the variables is given in table. 1.

Table 1

Dummy Variables Description

D30 Heated seats

D31 Headlight washer

D32 Wood grain finish

D33 Parktronic

D34 Front armrest

D35 Fog lights

D36 Div. backrest seats

D37 Regul. led. water. in height

D3S Regul. led. pass. in height

D39 Steering wheel adjustment

D40 Salon (velor)

D41 Salon (leather)

D42 Alarm

D43 Cell phone

D44 Tinted glass

D45 Tow bar

D46 central locking

D47 Electrical antenna

D4S Electric mirrors

D49 Electric actuator water seat (is)

D50 Electric drive water seats (with memory)

D51 Electric actuator pass. seat

D52 Electric glass (all)

D53 Electric windows (front)

D54 Radio (is)

D55 Radio (with OB)

D56 Radio (with MP3)

D57 SB-changer (is)

D5S SB-changer (with MP3)

SIMPLE PRICE MODEL OF A USED CAR

Consider the following regression equation

ln (PRICE) \u003d a + ^ PX + e. (1)

Here are the factors; a - some constant; Pi - unknown parameters; e is a random component that takes into account factors unaccounted for in the model and possible data errors. Parameters Pg- have the following meaning: with fixed values \u200b\u200bof the remaining factors, a change in the i-th factor per unit leads to a price change on average by Pg- x 100%

(about). The parameter a does not have any economic interpretation. Regression equation (1) can be used to build a price model for a certain car model. The construction consists in the estimation of the unknown parameters a and Pr- by the method of least squares.

The main problem here is to determine the "best" regression equation - the equation containing the largest number of significant factors, having the highest value of the coefficient of determination and having a consistent economic interpretation. To solve this problem, one can use the "private-to-general" and "general-to-specific" approaches, but, as you know, none of them guarantees that the model specification is economically correct. Therefore, when choosing between alternative models, preference should be given to the one that has a consistent economic interpretation.

We will consider the process of building a model using the example of a VAZ 2109 car. This model is produced in modifications with a sedan and a hatchback body type. Scatter diagrams price / age and price / mileage of the given coefficient of determination is 0.82, which

are shown in Fig. 1 and 2.says about enough good quality fit. As a first approximation, we construct the coefficient in front of the variable AGE shows that with

del, in which we include the following factors: an increase in the age of the car by one year its price

rast, mileage, body type and variable TORG. Re-, other things being equal, decreases on average

the results of estimating the parameters in econometric terms by 9.57%. Coefficient before variable PROBEG

package EViews are given in table. 2. shows that with an increase in mileage of 10,000 km

the price of a car, other things being equal, Table 2 decreases on average by 0.55%. The coefficient in front of the KZ1 variable shows that the hatchback model, all other things being equal, costs 9.16% less than the sedan model. The TORG variable turned out to be insignificant.

Let's add the factors D1, D2, ..., D58 to the model and re-evaluate the parameters, excluding successively insignificant factors in accordance with the "from general to particular" method. The evaluation result is shown in table. 3. As can be seen from the table, new model turned out better than the previous one: the adjusted coefficient of determination is 0.84. The coefficients before variables AGE, PROBEG and KZ1 remained significant and changed insignificantly. The coefficient in front of the TORG variable turned out to be

ALPHA AGE PROBEG KZ1 TORG 8.847406 -0.095726 -0.005521 -0.091577 0.012405 0.010334 856.1205 0.000967 -98.97453 0.000784 -7.043760 0.004708 -19.45046 0.008820 1.406509 0.0000 0.0000 0.0000 0.0000 0.1597

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat 0.823463 0.823253 0.135187 61.40574 1961.463 1.744911 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob (F-statistic) 8.274289 0.321558 -1.162831 -1.153736 3918.210 0.000000

0 4 8 12 16 20 24

Figure: 1. Price / Age Chart

12000 10000 8000

0 4 8 12 16 20 24

PROBEG Fig. 2. Price / mileage chart

As can be seen from the table, the value of the adjusted

Table 3

Dependent Variable: LOG (PRICE) Method: Least Squares Included observations: 3365

Variable Coefficient Std. Error t-Statistic Prob.

ALPHA 8.777030 0.011135 788.2484 0.0000

AGE -0.092950 0.000952 -97.66733 0.0000

PROBEG -0.007003 0.000756 -9.262149 0.0000

KZ1 -0.080293 0.004580 -17.53211 0.0000

TORG 0.023634 0.008443 2.799281 0.0052

D10 0.030518 0.005863 5.204758 0.0000

D13 0.034216 0.010227 3.345643 0.0008

D15 0.042650 0.013579 3.140945 0.0017

D22 0.024459 0.007286 3.356796 0.0008

D26 0.038207 0.005461 6.996574 0.0000

D35 0.016877 0.007272 2.320622 0.0204

D44 0.022819 0.004655 4.902135 0.0000

D45 0.027283 0.008625 3.163398 0.0016

D46 0.015448 0.004953 3.118926 0.0018

D47 0.025603 0.011280 2.269820 0.0233

R-squared 0.839828 Mean dependent var 8.274289

Adjusted R-squared 0.839159 S.D. dependent var 0.321558

S.E. of regression 0.128961 Akaike info criterion -1.254171

Sum squared resid 55.71335 Schwarz criterion -1.226885

Log likelihood 2125.143 F-statistic 1254.646

Durbin-Watson stat 1.879215 Prob (F-statistic) 0.000000

USED \u200b\u200bVEHICLE INDEX MODEL

Let Р0 be the price of a used car, and Рп - exactly the same new one. Consider the dimensionless quantity I \u003d ln (P0) / ln (Pn), which is called the index below. It is logical to assume that the change in the index is associated with the aging process of the car, i.e. depends on the time and intensity of car use:

I \u003d a + rLvB + URKOBBO + e.

Suppose also that the wear over time of cars different manufacturers happens in different ways:

I \u003d a + Y, Mß AGE + y PROBEG + e,

significant. It can be interpreted as follows: the seller who indicated the possibility of bargaining in the announcement, on average, overstates the price in advance by 2.36%. The coefficients in front of the variables from the equipment set turned out to be significant at the 5% level and positive, which corresponds to common sense (the presence in the car additional options should increase its value).

The plot of residuals (Fig. 3) shows that the forecast errors are randomly located around zero, which indicates in favor of the correct specification of the model. The average price forecast error was $ 318.73, or 8.58%. Note that the influence of each of the factors TORG, D10, D13, D15, D22,

D26, D35, D44, D45, D46, and D47 individually turned out to be less than the average forecast error, nevertheless, they are all significant at the 5% level and cannot be excluded from the model.

1.2 0.8 -0.4 -0.0 -0.4 -0.8 N -1.2 -1.6

where Mi is a dummy variable corresponding to the car brand; a, p, - and y are the estimated parameters.

The data available in the sample does not allow calculating the index, since it is not possible to find an identical new one for each used car. Therefore, the used car index will be calculated by calculating the value of Pp as the weighted average price of new cars of the same make and model. In the available sample, the indices were calculated for 28794 cars. The results of estimating the parameters of model (2) are given in table. 4.

Table 4

■ LOG (PRICE) Residuals

Figure: 3. Chart of balances

Dependent Variable: IDXPRICE Method: Least Squares Included observations: 28794

Variable Coefficient Std. Error t-Statistic Prob.

ALPHA 0.999821 0.000233 4290.870 0.0000

AGE * M0 -0.015290 0.000104 -147.1760 0.0000

AGE * M1 -0.014012 8.93E-05 -156.9820 0.0000

AGE * M2 -0.009440 0.000198 -47.58022 0.0000

AGE * M3 -0.014539 0.000686 -21.19981 0.0000

AGE * M4 -0.009960 0.000137 -72.94191 0.0000

AGE * M5 -0.010939 0.000169 -64.60249 0.0000

AGE * M6 -0.008104 0.000230 -35.22352 0.0000

AGE * M7 -0.011521 0.000216 -53.24322 0.0000

AGE * M8 -0.007242 0.000825 -8.773554 0.0000

AGE * M9 -0.013029 0.000106 -122.6546 0.0000

AGE * M10 -0.010993 0.000108 -101.7212 0.0000

AGE * M11 -0.011134 9.66E-05 -115.2724 0.0000

AGE * M12 -0.011676 8.54E-05 -136.7619 0.0000

AGE * M13 -0.012877 0.000314 -41.04783 0.0000

AGE * M14 -0.010665 0.000174 -61.13954 0.0000

AGE * M15 -0.016336 0.000240 -67.98064 0.0000

AGE * M16 -0.008689 0.000246 -35.28486 0.0000

AGE * M17 -0.011942 9.45E-05 -126.3381 0.0000

AGE * M18 -0.010433 7.76E-05 -134.3959 0.0000

AGE * M19 -0.013430 0.000241 -55.66306 0.0000

AGE * M20 -0.010890 5.55E-05 -196.3888 0.0000

AGE * M21 -0.019084 0.000119 -159.7062 0.0000

PROBEG -0.000795 3.46E-05 -22.98319 0.0000

R-squared 0.844103 Mean dependent var 0.932866

Adjusted R-squared 0.843979 S.D. dependent var 0.053447

S.E. of regression 0.021111 Akaike info criterion -4.877166

Sum squared resid 12.82264 Schwarz criterion -4.870274

Log likelihood 70240.56 F-statistic 6772.848

Durbin-Watson stat 1,350200 Prob (F-statistic) 0.000000

As you can see from the table, all the coefficients turned out to be significant. The value of the parameter a is close to one, which corresponds to the meaning of the index ( new car with zero mileage and zero age has an index equal to 1). The corrected coefficient of determination is 0, S4, the average forecast error of the index was 1.61%.

The result obtained allows us to build a rating of manufacturers according to the rate of decline in the car index with age: Mazda (-0.0072), Hyundai (-0.0081), Suzuki (-0.0086), Daewoo (-0.0094), Ford (-0 , 0099), Volkswagen (-0.0104), Renault (-0.0106), VAZ (-0.0108), Honda (-0.0109), Mitsubishi (-0.0109), Nissan (-0.0111 ), Lexus (-0.0115), Opel (-0.0116), Toyota (-0.0119), Peugeot (-0.0128), Mercedes (-0.0130), Volvo (-0.0134), BMW (-0.0140), Dodge (-0.0145), Audi (-0.0152), Subaru (-0.0163), GAS (-0.0190). Thus, the purchase of a Mazda car will be the most profitable for the buyer of a car who plans to sell it after a while.

CONCLUSION

The article discusses two models of the dependence of the price of a used car on the parameters. From the first model it follows that the main factor affecting the price of a car is its age. The rest of the factors have a less significant impact, including such a seemingly important factor as mileage, which is consistent with the opinion of experts (http://caragent.ru/info/odometer.shtm1). Nevertheless, they should not be neglected, since their cumulative contribution can be significant. We also add that the sample did not include and, therefore, did not include such important factors as the condition of the body, engine, interior and chassis, information about who is the owner and whether the car was in an accident. Perhaps taking them into account would make the model more accurate.

The second model made it possible to assess the qualitative difference between cars different manufacturers... Based on the results of evaluating the model, a rating of car manufacturers was built according to the rate of price decline with age.

LITERATURE

1. Magnus Ya.R., Katyshev P.K., Peresetskiy A.A. Econometrics. Initial course. M .: Delo, 2004.

2. Dougherty K. Introduction to econometrics. M .: INFRA-M, 2004.

3. Draper N., Smith G. Applied regression analysis. Moscow: Statistics, 1973.

The article is presented by the Department of Mathematical Methods and Information Technologies in Economics of the Faculty of Economics of Tomsk state university, entered the scientific editorial office of "Cybernetics" on May 31, 2005

Description

The deadline for submitting the report is 10 working days. The study is being sold with an update.

This research is a marketing analysis of the used car market in Russia. The company's analysts have made a forecast of market development until 2024.

Study period: 2015 - 2019

Object of study: used car market

Subject of study: market size, market trends for the sale of used cars, factors affecting the market, main competitors, consumer prices, industry financial and economic indicators, assessment of investment attractiveness, market development forecast and other processes

Purpose of the study: analysis and forecast of the development of the used car sales market

Research objectives:

• Description of the state of the used car market
• Assessment of the volume of the used car market
• Description of the main competitors
• Assessment of current trends and market development prospects
• Analysis of industry indicators of financial and economic activity
• Determination of market saturation and estimated market potential
• Making a forecast of market development until 2024

The main blocks of research:

• Overview of the Russian used car market
• Competitive analysis of the used car market in Russia
• Analysis of the consumption of used car sales
• Assessment of the factors of investment attractiveness of the market
• Forecast for the development of the used car market until 2024
• Conclusions on the prospects of creating enterprises in the studied area and recommendations to existing market operators

Sources of information:

• Databases of state statistical bodies
• Databases of the Federal Tax Service
• Open sources (sites, portals)
• Issuer reporting
• Company Sites
• Media archives
• Regional and federal media
• Insider Sources
• Specialized analytical portals

Methods:

• Desk research. Search and analysis of information from various sources, calculations. Statistics and analytics

• GidMarket forecast. Modern statistical forecasting methods adjusted for expert opinion.

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Content

Part 1. Overview of the Russian used car market

1.1. Definition and characteristics of the Russian used car market

1.2. Dynamics of the volume of the Russian market for the sale of used cars, 2015-2019

1.3. Market structure by type of rendered sale of used cars in Russia

1.4. The structure of the used car sales market by federal district

1.5. Assessment of current trends and development prospects of the studied market

1.6. Assessment of factors influencing the market

1.7. Analysis of industry indicators of financial and economic activity

Part 2. Competitive analysis of the used car market in Russia

2.1. The largest players in the market

2.2. Market shares of the largest competitors

2.3. Major player profiles

Part 3. Analysis of the consumption of used car sales

3.1. Estimation of consumption volume of used car sales per capita

3.2. Market saturation and estimated market potential in Russia

3.3. Description of consumer preferences

3.4. Price analysis

Part 4. Assessment of the factors of investment attractiveness of the market

Part 5. Forecast of the development of the used car market until 2024

Part 6. Conclusions on the prospects of creating enterprises in the study area and recommendations to existing market operators

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Illustrations

Chart 1.Dynamics of the volume of the used car sales market, 2015-2019

Chart 2.Structure of the used car sales market by type,%

Chart 3.Structure of sales of used cars in the Russian Federation by federal district,%

Chart 4.Dynamics of RF GDP, in 2012-2019,% to the previous year

Chart 5.Monthly dynamics of the US dollar against the ruble, 2015-2019, rubles for 1 USD

Chart 7.Dynamics of real incomes of the population of the Russian Federation, 2012-2019

Chart 8.The profitability of profit before tax (profit of the reporting period) in the sale of used cars in comparison with all sectors of the Russian economy, 2015-2019,%

Chart 9.Current liquidity (total coverage) by the used car sales industry for 2015-2019, times

Chart 10.Business activity (average period of turnover of receivables) in the sale of used cars, for 2015-2019, days days

Chart 11.Financial stability (provision with own circulating assets) in the sale of used cars, in comparison with all sectors of the Russian economy, 2015-2019,%

Chart 12.Shares of the largest competitors in the used car market in 2019

Chart 13.Dynamics of the total revenue of the largest operators of the used car sales market (TOP-5) in Russia, 2015-2019

Chart 14.Consumption of used car sales per capita, 2015-2019, RUB / person

Chart 15.Forecast of the volume of the used car sales market in 2020-2024

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Tables

Table 1. STEP analysis of factors affecting the used car market

Table 2.Gross profitability of the used car sales industry in comparison with all sectors of the Russian economy, 2015-2019,%

Table 3.Absolute liquidity of the used car sales industry in comparison with all sectors of the Russian economy, 2015-2019, times

Table 4.Main companies participating in the used car market in 2019

Table 5.Basic information about the # 1 participant in the used car market

Table 6.Basic information about the participant # 2 of the used car market

Table 7.Basic information about the participant # 3 of the used car market

Table 8.Basic information about the participant # 4 of the used car market

Table 9.Basic information about the participant # 5 of the used car market

Table 10.Consumer price indices in the used car market by Russian Federation in 2015-2020 (available period),%

Table 11.Average prices on the used car market in the Federal District

Table 12.Assessment of the factors of investment attractiveness of the used car market

Expand

Issues

The secondary car market, which was actively growing during the first half of the year, has sharply reduced its growth rates since July, and in September it even reached zero. So, in the first autumn month, Russians bought 461.5 thousand used cars (-0.1%), according to the agency "Autostat". Thus, the demand for used cars went slightly negative for the first time after seven months of growth. In general, in January-September, 3 million 835 used cars were replaced by owners, which is 7.4% more compared to last year.

According to Avito Auto experts, the Russian car market depends on macroeconomic indicators, primarily on the exchange rate of the national currency. As a rule, after each fall of the ruble, consumers buy cars for a month, expecting a jump in prices. In 2016, the ruble exchange rate stabilized, and the growth rate of the secondary market also slowed down.

“Seasonality is returning to the Russian car market, which was lost after the collapse of the ruble at the end of 2014. Last year the "auto spring" did not come, by the summer-autumn many potential buyers switched to used cars, sales of which began to grow rapidly. The seasonality has disappeared, and now everything is returning to its place, ”says Denis Eremenko, director of PodborAvto.

Indeed, the market for new cars in September markedly slowed down the pace of decline, while in previous months the sales curve was steadily creeping down. However, industry participants are in no hurry to talk about the beginning of stabilization of demand, and the September revival among buyers is more likely associated with rumors about the termination of state support programs. Maybe, the last chance to buy a new car with a good discount attracted many consumers from the secondary market.

“Many buyers place a new and a used car on different scales at the same time. And the renewed demand for new cars will negatively affect the dynamics of the secondary market. State support programs, discounts and special offers stimulate sales of new cars. Following the fall in demand for used cars, we will observe a correction in prices, however, one should not count on large discounts, since there is a paucity of offers, ”comments Denis Eremenko.

However, the situation on the new car market is unlikely to change dramatically in the near future: car prices are growing, but the purchasing power of the population is still falling, says Artem Samorodov, director of the Used Cars direction of the Independence Group. In his opinion, if sales of new cars continue to fall in 2017, the secondary car market will continue to show growth, luring customers and increasing the decline in the new car segment.

According to the joint forecast of PwC and Avito Auto, by the end of 2016 the sales of used cars will grow by 7% to 5.3 million units and will maintain positive dynamics next year. The market for new cars this year will shrink by 14% to 1.3 million units, and in 2017 it will slow down. Thus, for one new car sold, there are now 4.1 used cars sold. Experts associate the positive dynamics of the secondary car market, first of all, with the colossal difference in prices between new and used cars. In 2016, the weighted average price of a new car increased by 43.7% to 1 million 404 thousand rubles, while used cars rose by an average of 13.2% to 380 thousand rubles. However, over time, prices for the latter will also rise, and this will lead to the fact that the ratio of new and used cars will drift towards the standard for developed markets 2-2.5.

Foreign brands are not the first freshness

Unlike the new car market, on the secondary market there is a drop in sales domestic cars, and the drivers of demand are foreign cars... This is largely due to a decrease in sales lada cars and UAZ in the primary market in the past years, while the foreign car park increased, and now this ratio is reflected in the structure of used car sales. So, Lada, which, due to the huge fleet of vehicles and their cheapness, traditionally lead in terms of sales volume, showed a 3% decline in January-September, according to the data of Avtostat. GAZ went into negative territory by 16.7%, demand for all-terrain vehicles UAZ decreased by 2.7%. At the same time, sales of the most popular used foreign cars Toyota and Nissan increased by 12.6%, and the most significant positive dynamics in January-September showed korean Hyundai (+ 22.1%) and Kia (+ 28.3%), and chinese Lifan (+ 30.9%) and Geely (+ 36%).

Top 25 best-selling used car models in Russia (data of "Autostat")

№	Model	September 2016	Change,%	January-September 2016	Change,%
1.	Lada 2114	13 659	-6,8	115 588	1,2
2.	Lada 2107	12 519	-16,4	109 640	-10,7
3.	Ford focus	11 738	-0,3	94 969	12,6
4.	Lada 2110	10 992	-10,1	92 107	-6,8
5.	Toyota Corolla	9 191	1,7	77 450	10,9
6.	Lada 2170	8 873	6,1	72 725	13,5
7.	Lada 4x4	8 630	-4,1	71 565	1
8.	Lada 2112	8 092	-5,9	66 711	-3,8
9.	Lada 2115	7 782	-5,4	64 734	-1,6
10.	Lada 2109	7 005	-20,6	61 229	-17
11.	Toyota Camry	6 327	24,6	52 277	30,6
12.	Hyundai solaris	6 065	51,4	44 599	59,6
13.	Daewoo nexia	6 023	-2,3	49 652	2,1
14.	Chevrolet niva	5 853	6,1	48 528	16
15.	Renault Logan	5 825	-2,7	49 425	7,8
16.	Opel astra	5 611	6,2	44 494	18,1
17.	Lada 2106	5 557	-24,3	48 816	-20,7
18.	Lada 21099	5 494	-20,3	49 084	-16,4
19.	Volkswagen passat	5 266	3,4	42 693	8
20.	Mitsubishi lancer	4 980	3,7	41 482	10,5
21.	Kia rio	4 939	23,2	38 530	39,7
22.	Lada 2190	4 816	28,4	37 670	48,2
23.	Lada 2172	4 694	1,5	38 810	11,2
24.	Daewoo matiz	4 223	-3,1	33 751	5,5
25.	Skoda Octavia	3 798	8,3	30 462	21,3

According to Avito Auto experts, there is now a skew in the structure of the car market for new and used cars: the share of SUVs in sales of new cars is much higher than in the secondary market, in sales of used cars - the share of segments B and C, which account for the lion's share domestic brands... But sooner or later, new SUVs sold enter the secondary market, and their share inevitably increases, while the share of B + C segments decreases, and the demand for domestic cars with them.

As for the dynamics of prices, the greatest growth in value is shown mainly by cars aged 6-7 years, as well as up to 3 years. For instance, toyota cars 2009-2010 years of release are offered on average for 964 thousand rubles, for last year having risen in price by an average of 33%. A significant increase in cost was also recorded for hyundai cars the same age - their average price tag increased by 28% and amounted to 517.9 thousand rubles, they say in Avito Auto. BMW models that have been in operation for up to three years have added 28% in price and today cost an average of 2 million 463 thousand 800 rubles. “Since the fall of 2014, the cost of new“ Germans ”has risen by 30%, in approximately the same numbers we see an increase in their prices on the secondary market. Today you can sell a BMW purchased in 2014–2015 for the price of buying a new one. This is a unique situation in the automotive market, ”states Denis Eremenko.

But cars older than seven years in some cases even fell in price. So, the average cost chevrolet cars decreased by 18% to 248.6 thousand rubles, and Daewoo - by 2%, although they are already the most affordable on the market with an average price tag of 101.5 thousand rubles. “Buyers know that these brands have left the Russian market, so they started buying them less, fearing, for example, being left without spare parts,” says Alexander Gruzdev, director of GiPA Russia. Basically, cars of this age still increased in price - from 1.3% (Lada) to 14% (Audi). Cars of all other age groups also rose in price.

According to Artem Samorodov, used car prices depend on new car prices, but they react with a delay of 2-3 months, and this is more true for the dealer segment. As for older cars, they "hang" somewhere in the range of 400-600 thousand, and fluctuations in the pricing of new cars do not seriously affect them. This segment has its own harsh rules - real demand, objective condition, operational characteristics.

yesterday, 21:10

Used Car Forum 2020 postponed to July 22

Analytical agency "AUTOSTAT" informs that due to the current epidemiological situation in the country, we decided to postpone the conference on used cars "Used Car Forum 2020" to July 22 of this year.

• 

  How much did used cars become more expensive in Russia?

  According to the analytical agency "AUTOSTAT", the average price of a used car in our country in February 2020 amounted to 630 thousand rubles.

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  "Raised prices, removed discounts." Used cars will become much more expensive (Autonews.ru)

  Used cars will rise in price in proportion to new models. Dealers are already rejecting discounts and updating offers. Experts told when and what options in the secondary market will add in price.

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  More than half of used cars are registered with second and third owners

  Experts of the analytical agency "AUTOSTAT" in the course of market research passenger cars with a run found out that more than half (53%) of such cars in our country are registered to the second and third owners.

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  Fresh Auto opened a hub for the sale of used cars and a Ford dealership in Voronezh

  Fresh Auto opened in Voronezh the largest salon in the city for the sale and service used cars. At the same time, a new dealership Ford, which became the third enterprise of the brand after Volgograd and Rostov-on-Don in the portfolio of the automobile network.

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  Russian used car market in February 2020

  According to the data of the analytical agency AUTOSTAT, in February 2020 the market volume of used cars in Russia amounted to 407.5 thousand units. This is 11.1% more compared to the same period in 2019.

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  Used passenger car market up 11% in February

  According to the analytical agency "AUTOSTAT", the market volume of used cars in Russia at the end of February 2020 amounted to 407.5 thousand units, which is 11% higher than the result for the same period last year.

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  Dealers will survive in 2020 thanks to used cars

  An online survey was conducted among the participants of the annual forum of the automotive business "ForAuto - 2020", during which we found out what market experts think about the past year and what plans they are making for the future.